The present invention relates generally to neural networks, and more particularly, to a clustered neural network comprising a plurality of supervised learning rule back-propagation neural networks, and to a target detection system for use in a guided missile that employs such clustered neural networks.
One conventional neural network comprises a single back-propagation network. Back-propagation neural networks are generally described in the book entitled "Parallel Distributed Processing (PDP): Exploration in the Microstructure of Cognition," Volume 1, by D. E. Rumelhart, et al., published by MIT Press, Cambridge, Mass. (1986), and specifically at Chapter 8 entitled "Learning Internal Representations by Error Propagation" starting at page 318. The above-mentioned single back-propagation network is a conventional non-clustered neural network described at page 320 in the Rumelhart et al. reference, with specific reference to FIG. 1. Typically, the back-propagation network is unnecessarily large to learn a required task.
With reference to target detection systems for use in missiles, and the like, current systems have not employed neural networks to detect and classify targets located in an image scene.
Therefore, there is a need in the art for a neural network that is relatively small compared to a computationally comparable conventional back-propagation network. Furthermore, there is a need for a clustered neural network that provides for synergy that improves the properties of the clustered network over a comparably complex non-clustered networks. A comparably complex non-clustered network comprises a single back-propagation network having the same number of nodes as the total number of nodes in a clustered neural network. In addition, there is a need for a target detection system for use in a missile that is capable of providing real time target detection and guidance information employing such clustered neural networks.